AI Agents & LLM Integration

AI that acts, not just
answers questions

I build AI agents and LLM-powered features that integrate into your product — reliably, with guardrails, grounded in your own data.

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What I build

RAG Chatbots

AI assistants grounded in your documentation, knowledge base, or product data — accurate answers, no hallucinations.

Autonomous Agents

LLM agents with tool use: web search, code execution, database queries, API calls — completing tasks end-to-end.

AI-Powered Search

Semantic + hybrid search over your content using embeddings and vector databases (Pinecone, pgvector, Qdrant).

Document Intelligence

Extract structured data from PDFs, contracts, invoices, and forms — with validation and confidence scoring.

AI Feature Integration

Add LLM-powered features to your existing web app: auto-complete, summarisation, classification, generation.

Voice AI

Conversational voice agents with real-time STT/TTS pipelines using Whisper, ElevenLabs, or Azure Cognitive Services.

Models & Tools

I use the best tool for the job — not just the most popular one.

OpenAI GPT-4oClaude 3.5 / 4Llama 3MistralLangChainLangGraphLlamaIndexPineconepgvectorQdrantWhisperElevenLabsVercel AI SDKHugging FacePythonNode.js

How I approach AI projects

  1. 01

    Define the task & constraints

    What does success look like? What are the failure modes you cannot accept? I document these before touching any model.

  2. 02

    Prototype & evaluate

    A working prototype with eval metrics — not vibes. I measure accuracy, latency, and cost from day one.

  3. 03

    Build production guardrails

    Output validation, fallback paths, cost caps, logging, and monitoring. AI in production needs guardrails, not just prompts.

  4. 04

    Deploy & iterate

    Ship behind a feature flag. Collect real usage data. Improve prompts and retrieval with production signal.

Frequently Asked Questions

What is an AI agent, exactly?

An AI agent is an LLM (like GPT-4) given tools — web search, database queries, API calls, code execution — so it can complete multi-step tasks autonomously rather than just answering questions.

What models do you work with?

OpenAI (GPT-4o, o1), Anthropic Claude, Mistral, Llama 3, and Gemini. I recommend the best model for your latency, cost, and capability requirements.

How do you prevent hallucinations?

Through RAG (retrieval-augmented generation) grounded in your own data, output validation layers, and structured JSON outputs with schema enforcement.

Can you integrate a chatbot into my existing product?

Yes — as a React component, iframe embed, Slack/Teams bot, or via API. I handle the full integration including streaming responses and conversation memory.

Have an AI use case in mind?

I'll tell you honestly if it's a good fit for LLMs — and what the realistic scope looks like.

Let's Talk →